Scale By the Bay 2019

Machine learning has been described as "Software 2.0" and holds the promise of totally changing how software systems are constructed. But if manually-written "Software 1.0" code is still plagued with bugs, downtime, and security vulnerabilities, how can we hope to achieve reliable behavior in systems with significant data-dependent machine learning components and their attendant complexities? This talk will survey academic research, industry best practices, and software tools spanning the end-to-end development of machine learning systems from data pipelines to tests and types all the way through to end user experience.